Classification the geographical origin of corn distillers dried grains with solubles by near infrared reflectance spectroscopy combined with chemometrics: A feasibility study

Zhou, Xingfan; Yang, Zengling; Haughey, Simon A.; Galvin-King, Pamela; Han, Lujia; Elliott, Christopher T.

doi:10.1016/j.foodchem.2014.09.104

Cited by 16 publications

(12 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Results obtained herein were in agreement with those reported by other authors that applied the NIR spectroscopy in combination with chemometric analysis to the discrimination of common wheat grown from different regions of China [12,14,15] and Chile [16]. Other successful applications of NIR spectroscopy to the geographical discrimination of cereals and derived products include arabica coffee from Brazil [21,22] and corn distillers dried grains from various countries [13]. The majority of these papers combine NIR spectroscopy with the partial least squares discriminant analysis (PLS-DA) and, to lesser extent, with LDA chemometric model for the geographical discrimination of wheat.…”

Section: Resultssupporting

confidence: 90%

“…Furthermore, IR spectroscopy, in combination with multivariate analysis, can enhance the information generated by the analysis of samples, allowing for a detection of a pattern in a data set, and for the development of mathematical models to monitor authenticity and traceability [8,9]. IR spectroscopy has been successfully applied to several applications in the field of identification of geographical origin, such as virgin olive oil, cheese, wine, honey, tea, lentils, distillers dried grains with solubles, and other food products [7,9,10,11,12,13]. However, only a few applications of IR spectroscopy to differentiate wheat samples for geographical origin have been described in the literature.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Tracing the Geographical Origin of Durum Wheat by FT-NIR Spectroscopy

Girolamo

Cortese

Cervellieri

et al. 2019

Foods

View full text Add to dashboard Cite

Fourier transform near infrared (FT-NIR) spectroscopy, in combination with principal component-linear discriminant analysis (PC-LDA), was used for tracing the geographical origin of durum wheat samples. The classification model PC-LDA was applied to discriminate durum wheat samples originating from Northern, Central, and Southern Italy (n = 181), and to differentiate Italian durum wheat samples from those cultivated in other countries across the world (n = 134). Developed models were validated on a separated set of wheat samples. Different pre-treatments of spectral data and different spectral regions were selected and compared in terms of overall discrimination (OD) rates obtained in validation. The LDA models were able to correctly discriminate durum Italian wheat samples according to their geographical origin (i.e., North, Central, and South) with OD rates of up of 96.7%. Better results were obtained when LDA models were applied to the discrimination of Italian durum wheat samples from those originating from other countries across the world, having OD rates of up to 100%. The excellent results obtained herein clearly indicate the potential of FT-NIR spectroscopy to be used for the discrimination of durum wheat samples according to their geographical origin.

show abstract

Section: Resultssupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Tracing the Geographical Origin of Durum Wheat by FT-NIR Spectroscopy

Girolamo

Cortese

Cervellieri

et al. 2019

Foods

View full text Add to dashboard Cite

show abstract

“…Several models were established to figure out the geographical origin of corn distillers dried grains with solubles, amazonian tree species and hazelnut, wheat and the different floral origins of Chinese honey samples by NIR spectroscopy [13–14,22–25]. Leaves are complex assemblies of organic compounds and, because of this, may be expected to exhibit different spectral responses.…”

Section: Discussionmentioning

confidence: 99%

Application of visible and near-infrared spectroscopy to classification of Miscanthus species

Jin¹,

Chen²,

Xiao³

et al. 2017

PLoS ONE

View full text Add to dashboard Cite

The feasibility of visible and near infrared (NIR) spectroscopy as tool to classify Miscanthus samples was explored in this study. Three types of Miscanthus plants, namely, M. sinensis, M. sacchariflorus and M. fIoridulus, were analyzed using a NIR spectrophotometer. Several classification models based on the NIR spectra data were developed using line discriminated analysis (LDA), partial least squares (PLS), least squares support vector machine regression (LSSVR), radial basis function (RBF) and neural network (NN). The principal component analysis (PCA) presented rough classification with overlapping samples, while the models of Line_LSSVR, RBF_LSSVR and RBF_NN presented almost same calibration and validation results. Due to the higher speed of Line_LSSVR than RBF_LSSVR and RBF_NN, we selected the line_LSSVR model as a representative. In our study, the model based on line_LSSVR showed higher accuracy than LDA and PLS models. The total correct classification rates of 87.79 and 96.51% were observed based on LDA and PLS model in the testing set, respectively, while the line_LSSVR showed 99.42% of total correct classification rate. Meanwhile, the lin_LSSVR model in the testing set showed correct classification rate of 100, 100 and 96.77% for M. sinensis, M. sacchariflorus and M. fIoridulus, respectively. The lin_LSSVR model assigned 99.42% of samples to the right groups, except one M. fIoridulus sample. The results demonstrated that NIR spectra combined with a preliminary morphological classification could be an effective and reliable procedure for the classification of Miscanthus species.

show abstract

“…In contrast, spectroscopic methods such as near-infrared spectroscopy (NIRS) provide rapid evaluation of the internal qualities of agricultural products based on the vibrational motions of organic molecules. Thus, spectroscopic techniques have been applied for the evaluation of the internal quality of agricultural products such as species discrimination [7,8,9,10], nutrient analysis [11,12,13], and internal defect detection [3,14,15,16,17,18,19].…”

Section: Introductionmentioning

confidence: 99%

Multispectral Fluorescence Imaging Technique for On-Line Inspection of Fecal Residues on Poultry Carcasses

Seo

Lee

et al. 2019

Sensors

View full text Add to dashboard Cite

Rapid and reliable inspection of food is essential to ensure food safety, particularly in mass production and processing environments. Many studies have focused on spectral imaging for poultry inspection; however, no research has explored the use of multispectral fluorescence imaging (MFI) for on-line poultry inspection. In this study, the feasibility of MFI for on-line detection of fecal matter from the ceca, colon, duodenum, and small intestine of poultry carcasses was investigated for the first time. A multispectral line-scan fluorescence imaging system was integrated with a commercial poultry conveying system, and the images of chicken carcasses with fecal contaminants were scanned at processing line speeds of one, three, and five birds per second. To develop an optimal detection and classification algorithm to distinguish upper and lower feces-contaminated parts from skin, the principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) were first performed using the spectral data of the selected regions, and then applied in spatial domain to visualize the feces-contaminated area based on binary images. Our results demonstrated that for the spectral data analysis, both the PCA and PLS-DA can distinguish the high and low feces-contaminated area from normal skin; however, the PCA analysis based on selected band ratio images (F630 nm/F600 nm) exhibited better visualization and discrimination of feces-contaminated area, compared with the PLS-DA-based developed chemical images. A color image analysis using histogram equalization, sharpening, median filter, and threshold value (1) demonstrated 78% accuracy. Thus, the MFI system can be developed utilizing the two band ratios for on-line implementation for the effective detection of fecal contamination on chicken carcasses.

show abstract

Classification the geographical origin of corn distillers dried grains with solubles by near infrared reflectance spectroscopy combined with chemometrics: A feasibility study

Cited by 16 publications

References 33 publications

Tracing the Geographical Origin of Durum Wheat by FT-NIR Spectroscopy

Tracing the Geographical Origin of Durum Wheat by FT-NIR Spectroscopy

Application of visible and near-infrared spectroscopy to classification of Miscanthus species

Multispectral Fluorescence Imaging Technique for On-Line Inspection of Fecal Residues on Poultry Carcasses

Contact Info

Product

Resources

About